Location: Austin - Texas
Job DescriptionPosition Summary: A role that develops (data extraction, algorithmic, GUI, control system integration), tests (lab environment testing) and deploys AI-based solutions (internal and external pilot-type activities).Responsibilities: Develop and deploy end-to-end (including data extraction, algorithm development, control system integration and GUI) AI-based solutions and architectures.Apply an understanding of complex control systems and data analytics to produce a data-focused approach to Industrial Automation problem cases.Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc.Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sourcesIdentify processes that improve data reliability, efficiency, and quality.Conduct research for industry and business questions.Collaborates with product owners and stakeholders to ensure alignment of architecture with business prioritiesProactively monitors industry trends and identifies opportunities to implement new technologiesCreates and executes required test scenarios/plans to ensure complete testing on all new and changed componentsHelps to create a culture of openness, honesty, and transparencyBuilds strong relationships with technical and non-technical stakeholders across the organization Basic QualificationsDegree (Minimum of B.Sc. M.Sc. or Ph.D is a plus) in Engineering, Physics, or Computer ScienceLegal authorization to work in the US is required. We will not sponsor individuals for employment visas, now or in the future, for this job opening.Preferred Qualifications Typically requires three years of related work experienceProgramming background with languages such as Python, C/C++. Aptitude to quickly learn new programming language as need arises.Familiarity with automation applications (control, monitoring, ... especially real-time deployment)Background in one or more of the following disciplines: electrical engineering, computer science, industrial engineering, chemical engineering, data science.Background in machine learning. Aptitude to quickly learn new ML concepts as need arises.Familiarity with real-world datasets
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